Deep Learning is already changing the way computers see, hear and identify objects in the real world.
However, the bigger — and perhaps more pertinent — issues for the semiconductor industry are: Will “deep learning” ever migrate into smartphones, wearable devices, or the tiny computer vision SoCs used in highly automated cars? Has anybody come up with SoC architecture optimized for neural networks? If so, what does it look like?
“There is no question that deep learning is a game-changer,” said Jeff Bier, a founder of the Embedded Vision Alliance. In computer vision, for example, deep learning is very powerful. “The caveat is that it’s still an empirical field. People are trying different things,” he said.
There’s ample evidence to support chip vendors’ growing enthusiasm for deep learning, and more specifically, convolutional neural networks (CNN). CNN are widely used models for image and video recognition.
Earlier this month, Qualcomm introduced its “Zeroth platform,” a cognitive-capable platform that’s said to “mimic the brain.” It will be used for future mobile chips, including its forthcoming Snapdragon 820, according to Qualcomm.
Cognivue is another company vocal about deep learning. The company claims that its new embedded vision SoC architecture, called Opus, will take advantage of deep learning advancements to increase detection rates dramatically. Cognivue is collaborating with the University of Ottawa.
If presentations at Nvidia’s recent GPU Technology Conference (GTC) were any indication, you get the picture that Nvidia is banking on the all aspects of deep learning in which GPU holds the key.
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